Much has been achieved in the field of AI, yet much remains to be done if we are to reach the goals we all imagine. One of the key challenges with moving ahead is closing the gap between logical and statistical AI. Logical AI has mainly focused on complex representations, and statistical AI on uncertainty. Intelligent agents, however, must be able to handle both the complexity and the uncertainty of the real world.

Recent years have seen an explosion of successes in combining probability and (subsets of) first-order logic respectively programming languages and databases in several subfields of AI: reasoning, learning, knowledge representation, planning, databases, NLP, robotics, vision, and so on. Nowadays, we can learn probabilistic relational models automatically from millions of inter-related objects. We can generate optimal plans and learn to act optimally in uncertain environments involving millions of objects and relations among them. Exploiting shared factors can speed up messagepassing algorithms for relational inference but also for classical propositional inference such as solving SAT problems. We can even perform lifted probabilistic inference avoiding explicit state enumeration by manipulating first-order state representations directly.

So far, however, the researchers combining logic and probability in each of these subfields have been working mostly independently. We believe the current situation actually provides us with an opportunity for attempts at synthesis, forming a common core of problems and ideas, and crosspollinating across subareas. We explored the minimal perturbations required for each of the AI subfields to start using statistical relational (SR) techniques.

Thus, the goal of the StarAI workshop was to reach out to the general field of AI and to explore what might be called statistical relational AI.